1. Segmentation of Abdominal Adipose Tissues via Deep Learning (基于深度学习的腹内脂肪分割问题）
Fei Jiang

2. Abdominal Adipose Tissues Segmentation
Task
(a) MR image (b) segmentation
Volumes of visceral adipose tissues (VAT) and subcutaneous adipose tissues (SAT) are better indicator of obesity-related diseases than BMI.

3. Background: AAT Segmentation
39%（2014）
Harm
1.9 billiion
Metabolic and
cardiovascular diseases,
Liver cancer…
overweight
Diagnosis
SliceOmatic: 4,000$
模板来自于
Time- consuming
limitation：semi-automatic
time：6-8min/slice
Time-consuming, object-biase
Not practical, expensive

4. Segmentation of Abdominal Adipose Tissues
Flowchart of Previous methods
Three steps: (1) abdomen mask; (2) separating SAT; (3) separating VAT
Previous methods
Graph cut; Active contour; thresholding;…
shortcomings
Time-consuming re-training process for a new image
Two steps for separating SAT and VAT, extra errors

5. Our methods: AAT Segmentation
Pixel-based classification
Novelties:
task-based algorithm. Once the classifier is trained, it could be directly applied to a new image.
SAT and VAT separated by one step, no extra errors
automatic, unsupervised feature representation learned by DNN
Segmentation time: a few seconds/slice
Accuracy: Dice Ratio: SAT: 0.94+/-0.02; VAT: 0.88+/-0.04

6. Results: AAT segmentation

7. Tasks to do
Software to automatic segmentation of abdominal adipose tissues
Segmentation of 3-D abdominal adipose tissues

8. Automatic detection of lung module

9. Samples of lung and modules
Image of lung
modules

10. Previous methods for detecting lung module

11. Tasks to do
肺结节半自动检测
肺结节自动检测
肺结节自动检测软件